Lung CancerARCADESNCT05968898
Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Pulmonary nodulES
A study testing Optellum Virtual Nodule Clinic in people with Lung Cancer and Pulmonary Nodule, Solitary, run by Abramson Cancer Center at Penn Medicine at 3 sites in United States.
Trial summary
This is a pragmatic clinical trial that will study the effect of a radiomics-based computer-aided diagnosis (CAD) tool on clinicians' management of pulmonary nodules (PNs) compared to usual care. Read more about this study
- Who is it for?
- Ages 35 to 89 years
- Conditions the study is for
- Lung Cancer, Pulmonary Nodule, Solitary
- What is being tested?
- Optellum Virtual Nodule Clinic
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 300 participants
- Where
- 3 sitesUnited States
- Study dates
- 9 Jan 2024to 31 Jul 2030
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 35 to 89 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Male or female, aged 35-89 years
- Scheduled to be evaluated at a UPHS PN clinic
- Newly discovered solid or part-solid indeterminate PN 8-30mm in maximal diameter on CT imaging within 60 days of index clinic visit
- Chest CT imaging meeting the technical requirements for compatibility with Optellum Virtual Nodule Clinic software
Who cannot join
- Chest CT imaging with discrete mediastinal or hilar lymphadenopathy by CT size criteria (>10mm in maximal short-axis diameter on axial CT images)
- PNs with popcorn calcification (consistent with benign etiology)
- Pure ground-glass subsolid PNs (may be associated with lower risk of clinically significant malignancy)
- PN previously seen on CT imaging >60 days prior to most recent CT
- More than one indeterminate PN 8-30mm in maximal diameter
- History of lung cancer
- History of active cancer within the previous 5 years
- Presence of a thoracic implant that impedes PN visualization
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United States3 sites, 3 recruiting
- Recruiting
Penn Medicine University City
Philadelphia, Pennsylvania, 19104
Roger Kim, MD, MSCE
- Recruiting
Perelman Center for Advanced Medicine
Philadelphia, Pennsylvania, 19104
Roger Kim, MD, MSCE
- Recruiting
Penn Medicine Washington Square
Philadelphia, Pennsylvania, 19107
Roger Kim, MD, MSCE
About this study
Official title: Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules
This is a pragmatic clinical trial that will study the effect of a radiomics-based computer-aided diagnosis (CAD) tool on clinicians' management of pulmonary nodules (PNs) compared to usual care. Adults aged 35-89 years with 8-30mm PNs evaluated at Penn Medicine PN clinics will undergo 1:1 randomization to one of two groups, defined by the PN malignancy risk stratification strategy used by evaluating clinicians: 1) usual care or 2) usual care + use of a radiomics-based CAD tool.
Read the detailed description
Accurate malignancy risk stratification of pulmonary nodules (PNs) is critical to ensuring that cancer is diagnosed in a timely manner and patients do not undergo unnecessary diagnostic procedures. Preliminary data suggests that a radiomics-based lung cancer prediction (LCP) computer-aided diagnosis (CAD) tool is effective in risk stratifying PNs and may improve clinicians' PN management decisions. This is a pragmatic clinical trial evaluating the effect of this CAD tool on clinicians' management of PNs compared to usual care. Individuals eligible for this study will include adults aged 35-89 years who are scheduled to be evaluated at a Penn Medicine PN clinic for a newly discovered PN 8-30mm in maximal diameter on CT imaging. Exclusion criteria include lack of CT imaging data at the time of index clinic visit, thoracic lymphadenopathy by CT size criteria, presence of pulmonary masses (>3cm in maximal diameter), PNs with popcorn calcification (consistent with benign etiology), pure ground-glass subsolid PNs, a history of lung cancer, and history of any active cancer within 5 years. Enrolled participants will undergo 1:1 stratified randomization to one of two groups, defined by the PN malignancy risk stratification strategy used by evaluating clinicians: 1) usual care (clinician assessment) or 2) clinician assessment + CAD-based risk stratification using the LCP-CAD tool. The control arm will be usual care, defined as routine clinician assessment of PN malignancy risk. In the experimental arm, clinicians will be provided a report with the CAD tool estimate of malignancy risk for the PN being evaluated.
- Conditions
- Lung Cancer, Pulmonary Nodule, Solitary
- Being tested
- Optellum Virtual Nodule Clinic
- Keywords
- clinical effectiveness, clinical utility, artificial intelligence, medical decision-making, risk stratification
- Medical subject headings
- Lung Neoplasms, Solitary Pulmonary Nodule
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Optellum Virtual Nodule ClinicDevice
The Optellum Virtual Nodule Clinic is an FDA-approved (Class II) device for risk stratification of pulmonary nodules. It uses a convolutional neural network to evaluate CT imaging data to provide an estimate of malignancy risk for indeterminate pulmonary nodules.
| Study group | Role | What they receive |
|---|---|---|
| Usual care (clinician assessment) | No intervention | In the usual care arm, clinicians will evaluate individuals with indeterminate pulmonary nodules as part of routine clinical care. No specific guidance regarding pulmonary nodule risk stratification will provided to evaluating clinicians. |
| Clinician assessment + CAD-based risk stratification | Experimental | In the experimental arm, evaluating clinicians will receive a Lung Cancer Prediction report from an artificial intelligence radiomics-based computer-aided diagnosis tool for risk stratification of pulmonary nodules. Receives: Device: Optellum Virtual Nodule Clinic |
What is being measured
The primary outcome is the main question the study is designed to answer.
Appropriate management of pulmonary nodule
The composite proportion of benign pulmonary nodules managed with imaging surveillance and malignant pulmonary nodules managed with biopsy or empiric treatment. Final pulmonary nodule diagnosis will be categorized as malignant or benign based on pathologic evaluation. If pathology is unavailable or inconclusive (i.e., the biopsy was non-diagnostic), pulmonary nodule resolution, shrinkage, or diameter stability at 12 months will be defined as a benign diagnosis.
Measured: 12 months
Secondary outcomes (4)
Timeliness of care
For patients with malignant pulmonary nodules, defined as the number of days between the index clinic visit and diagnosis of malignancy and receipt of treatment for malignancy (i.e., surgical resection, radiation therapy).
Measured: 12 months
Adverse events
For patients undergoing biopsy, defined as procedural complications related to pulmonary nodule biopsy.
Measured: 12 months
Diagnostic yield
Using information found in pathology reports, defined as the proportion of biopsies with a definitive histopathologic diagnosis, for each type of diagnostic biopsy procedure.
Measured: 12 months
Healthcare costs
The costs of all imaging studies and diagnostic testing associated with the pulmonary nodule diagnostic process, based on Medicare allowed amounts (amount paid by Medicare and the amount paid by the beneficiary and/or third parties).
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Aug 2023
- Study start9 Jan 2024
- Primary completion31 Jul 2029 (estimated)
- Study completion31 Jul 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Abramson Cancer Center at Penn Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Roger Y. Kim, Abramson Cancer Center at Penn Medicine
- Investigators
- Roger Y. Kim, MD, MSCE, Principal investigator, University of Pennsylvania
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05968898
- Sponsor's protocol ID
- 10523
- Acronym
- ARCADES
- Status last verified
- Jun 2026
- First submitted
- 21 Jul 2023
- Last updated
- 2 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Publications
Kim RY, Oke JL, Pickup LC, Munden RF, Dotson TL, Bellinger CR, Cohen A, Simoff MJ, Massion PP, Filippini C, Gleeson FV, Vachani A. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology. 2022 Sep;304(3):683-691. doi: 10.1148/radiol.212182. Epub 2022 May 24.
PubMed 35608444Kim RY, Oke JL, Dotson TL, Bellinger CR, Vachani A. Effect of an artificial intelligence tool on management decisions for indeterminate pulmonary nodules. Respirology. 2023 Jun;28(6):582-584. doi: 10.1111/resp.14502. Epub 2023 Apr 5. No abstract available.
PubMed 37017091Massion PP, Antic S, Ather S, Arteta C, Brabec J, Chen H, Declerck J, Dufek D, Hickes W, Kadir T, Kunst J, Landman BA, Munden RF, Novotny P, Peschl H, Pickup LC, Santos C, Smith GT, Talwar A, Gleeson F. Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules. Am J Respir Crit Care Med. 2020 Jul 15;202(2):241-249. doi: 10.1164/rccm.201903-0505OC.
PubMed 32326730Baldwin DR, Gustafson J, Pickup L, Arteta C, Novotny P, Declerck J, Kadir T, Figueiras C, Sterba A, Exell A, Potesil V, Holland P, Spence H, Clubley A, O'Dowd E, Clark M, Ashford-Turner V, Callister ME, Gleeson FV. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax. 2020 Apr;75(4):306-312. doi: 10.1136/thoraxjnl-2019-214104. Epub 2020 Mar 5.
PubMed 32139611Paez R, Kammer MN, Balar A, Lakhani DA, Knight M, Rowe D, Xiao D, Heideman BE, Antic SL, Chen H, Chen SC, Peikert T, Sandler KL, Landman BA, Deppen SA, Grogan EL, Maldonado F. Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules. Sci Rep. 2023 Apr 15;13(1):6157. doi: 10.1038/s41598-023-33098-y.
PubMed 37061539Paez R, Kammer MN, Tanner NT, Shojaee S, Heideman BE, Peikert T, Balbach ML, Iams WT, Ning B, Lenburg ME, Mallow C, Yarmus L, Fong KM, Deppen S, Grogan EL, Maldonado F. Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules. Chest. 2023 Oct;164(4):1028-1041. doi: 10.1016/j.chest.2023.05.025. Epub 2023 May 25.
PubMed 37244587Kim RY. Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime? Cancer Biomark. 2025 Jan;42(1):CBM230360. doi: 10.3233/CBM-230360. Epub 2024 Feb 6.
PubMed 38427470
Study at a glance
From the registry record, last updated by the sponsor on 2 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules
- ClinicalTrials.gov ID
- NCT05968898
- Acronym / sponsor ID
- ARCADES / 10523
- Conditions
- Lung Cancer, Pulmonary Nodule, Solitary
- Keywords
- clinical effectiveness, clinical utility, artificial intelligence, medical decision-making, risk stratification
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Abramson Cancer Center at Penn MedicineUniversity, hospital or other
- Study start
- 9 Jan 2024 (actual)
- Primary completion
- 31 Jul 2029 (estimated)
- Study completion
- 31 Jul 2030 (estimated)
- First posted
- 1 Aug 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This is a pragmatic clinical trial that will study the effect of a radiomics-based computer-aided diagnosis (CAD) tool on clinicians' management of pulmonary nodules (PNs) compared to usual care. Adults aged 35-89 years with 8-30mm PNs evaluated at Penn Medicine PN clinics will undergo 1:1 randomization to one of two groups, defined by the PN malignancy risk stratification strategy used by evaluating clinicians: 1) usual care or 2) usual care + use of a radiomics-based CAD tool.
Detailed description
Accurate malignancy risk stratification of pulmonary nodules (PNs) is critical to ensuring that cancer is diagnosed in a timely manner and patients do not undergo unnecessary diagnostic procedures. Preliminary data suggests that a radiomics-based lung cancer prediction (LCP) computer-aided diagnosis (CAD) tool is effective in risk stratifying PNs and may improve clinicians' PN management decisions. This is a pragmatic clinical trial evaluating the effect of this CAD tool on clinicians' management of PNs compared to usual care. Individuals eligible for this study will include adults aged 35-89 years who are scheduled to be evaluated at a Penn Medicine PN clinic for a newly discovered PN 8-30mm in maximal diameter on CT imaging. Exclusion criteria include lack of CT imaging data at the time of index clinic visit, thoracic lymphadenopathy by CT size criteria, presence of pulmonary masses (>3cm in maximal diameter), PNs with popcorn calcification (consistent with benign etiology), pure ground-glass subsolid PNs, a history of lung cancer, and history of any active cancer within 5 years. Enrolled participants will undergo 1:1 stratified randomization to one of two groups, defined by the PN malignancy risk stratification strategy used by evaluating clinicians: 1) usual care (clinician assessment) or 2) clinician assessment + CAD-based risk stratification using the LCP-CAD tool. The control arm will be usual care, defined as routine clinician assessment of PN malignancy risk. In the experimental arm, clinicians will be provided a report with the CAD tool estimate of malignancy risk for the PN being evaluated.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Usual care (clinician assessment) | No intervention | –In the usual care arm, clinicians will evaluate individuals with indeterminate pulmonary nodules as part of routine clinical care. No specific guidance regarding pulmonary nodule risk stratification will provided to evaluating clinicians. |
| Clinician assessment + CAD-based risk stratification | Experimental | Device: Optellum Virtual Nodule ClinicIn the experimental arm, evaluating clinicians will receive a Lung Cancer Prediction report from an artificial intelligence radiomics-based computer-aided diagnosis tool for risk stratification of pulmonary nodules. |
Who may qualify
- Ages
- 35 to 89 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Male or female, aged 35-89 years
- Scheduled to be evaluated at a UPHS PN clinic
- Newly discovered solid or part-solid indeterminate PN 8-30mm in maximal diameter on CT imaging within 60 days of index clinic visit
- Chest CT imaging meeting the technical requirements for compatibility with Optellum Virtual Nodule Clinic software
Key exclusion criteria (8 listed)
- Chest CT imaging with discrete mediastinal or hilar lymphadenopathy by CT size criteria (>10mm in maximal short-axis diameter on axial CT images)
- PNs with popcorn calcification (consistent with benign etiology)
- Pure ground-glass subsolid PNs (may be associated with lower risk of clinically significant malignancy)
- PN previously seen on CT imaging >60 days prior to most recent CT
- More than one indeterminate PN 8-30mm in maximal diameter
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Optellum Virtual Nodule ClinicThe Optellum Virtual Nodule Clinic is an FDA-approved (Class II) device for risk stratification of pulmonary nodules. It uses a convolutional neural network to evaluate CT imaging data to provide an estimate of malignancy risk for indeterminate pulmonary nodules. | Device | Clinician assessment + CAD-based risk stratification |
Primary outcome
Appropriate management of pulmonary nodule
Time frame: 12 months
The composite proportion of benign pulmonary nodules managed with imaging surveillance and malignant pulmonary nodules managed with biopsy or empiric treatment. Final pulmonary nodule diagnosis will be categorized as malignant or benign based on pathologic evaluation. If pathology is unavailable or inconclusive (i.e., the biopsy was non-diagnostic), pulmonary nodule resolution, shrinkage, or diameter stability at 12 months will be defined as a benign diagnosis.
4 secondary outcomes
Timeliness of care
Time frame: 12 months
Adverse events
Time frame: 12 months
Diagnostic yield
Time frame: 12 months
Healthcare costs
Time frame: 12 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Jun 2026.
- Background: PubMed 35608444Kim RY, Oke JL, Pickup LC, Munden RF, Dotson TL, Bellinger CR, Cohen A, Simoff MJ, Massion PP, Filippini C, Gleeson FV, Vachani A. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology. 2022 Sep;304(3):683-691. doi: 10.1148/radiol.212182. Epub 2022 May 24.
- Background: PubMed 37017091Kim RY, Oke JL, Dotson TL, Bellinger CR, Vachani A. Effect of an artificial intelligence tool on management decisions for indeterminate pulmonary nodules. Respirology. 2023 Jun;28(6):582-584. doi: 10.1111/resp.14502. Epub 2023 Apr 5. No abstract available.
- Background: PubMed 32326730Massion PP, Antic S, Ather S, Arteta C, Brabec J, Chen H, Declerck J, Dufek D, Hickes W, Kadir T, Kunst J, Landman BA, Munden RF, Novotny P, Peschl H, Pickup LC, Santos C, Smith GT, Talwar A, Gleeson F. Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules. Am J Respir Crit Care Med. 2020 Jul 15;202(2):241-249. doi: 10.1164/rccm.201903-0505OC.
- Background: PubMed 32139611Baldwin DR, Gustafson J, Pickup L, Arteta C, Novotny P, Declerck J, Kadir T, Figueiras C, Sterba A, Exell A, Potesil V, Holland P, Spence H, Clubley A, O'Dowd E, Clark M, Ashford-Turner V, Callister ME, Gleeson FV. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax. 2020 Apr;75(4):306-312. doi: 10.1136/thoraxjnl-2019-214104. Epub 2020 Mar 5.
- Background: PubMed 37061539Paez R, Kammer MN, Balar A, Lakhani DA, Knight M, Rowe D, Xiao D, Heideman BE, Antic SL, Chen H, Chen SC, Peikert T, Sandler KL, Landman BA, Deppen SA, Grogan EL, Maldonado F. Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules. Sci Rep. 2023 Apr 15;13(1):6157. doi: 10.1038/s41598-023-33098-y.
- Background: PubMed 37244587Paez R, Kammer MN, Tanner NT, Shojaee S, Heideman BE, Peikert T, Balbach ML, Iams WT, Ning B, Lenburg ME, Mallow C, Yarmus L, Fong KM, Deppen S, Grogan EL, Maldonado F. Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules. Chest. 2023 Oct;164(4):1028-1041. doi: 10.1016/j.chest.2023.05.025. Epub 2023 May 25.
- Background: PubMed 38427470Kim RY. Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime? Cancer Biomark. 2025 Jan;42(1):CBM230360. doi: 10.3233/CBM-230360. Epub 2024 Feb 6.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Roger Y. Kim, MD, MSCE principal investigator
University of Pennsylvania
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Optellum Virtual Nodule Clinic in people with Lung Cancer and Pulmonary Nodule, Solitary, run by Abramson Cancer Center at Penn Medicine at 3 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 2 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Roger Y. Kim, MD, MSCE (215-662-3677, roger.kim@pennmedicine.upenn.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Appropriate management of pulmonary nodule (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05968898, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Jun 2026.